Particle swarm optimization for programming deep brain stimulation arrays

Abstract

Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies of <1% between approaches. Significance The PSO algorithm provides a computationally efficient way to program DBS systems especially those with higher electrode counts.

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

“…This allows clinicians to base their DBS programming on reliable neuroanatomical information and to limit the amount of trial and error programming needed to achieve optimal stimulation outcome. Techniques like these are also important for the emerging field of computer-assisted programming [22-24] for which reliable identification of the lead’s location and orientation is a mandatory prerequisite.…”

Determining the orientation angle of directional leads for deep brain stimulation using computed tomography and digital x‐ray imaging: A phantom study

“…Furthermore, such knowledge will be a prerequisite in transferring stimulation patterns to the patient's anatomy and help in interpretation of observed stimulation effects. Finally, the orientation angle will be mandatorily required as an input for computer‐based methods to characterize the effects of stimulation parameter adjustment and to optimize stimulation parameters …”

“…Finally, the orientation angle will be mandatorily required as an input for computer-based methods to characterize the effects of stimulation parameter adjustment and to optimize stimulation parameters. [8][9][10] To facilitate determination of the lead angle, a passive directional marker is added close to the electrodes by the manufacturers. This directional marker can be imaged with computed tomography (CT) and stereoscopic radiography, two standard imaging procedures used during and after stereotactic surgery to aid probe placement and to determine anatomical placement of the electrodes.…”

Systems approaches to optimizing deep brain stimulation therapies in Parkinson’s disease

“…Solutions proposed in (Butson et al, , ; Frankemolle et al, ; Lehto et al, ; Pena et al, ; Shamir et al, , ; Xiao et al, ) offer several advantages. First, they may assist clinicians during the DBS programming sessions and eventually reduce the duration of such sessions.…”